A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation

Xinyi Le; Jun Wang

doi:10.1007/978-3-642-42054-2_36

A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems is presented in this paper. The problem is reformulated from a quasi-convex optimization problem into a convex feasibility problem with the spectral condition number as the robustness measure. Two coupled globally convergent recurrent neural networks are applied for solving the reformulated problem in real time. Robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Simulation results of the proposed neurodynamic approach are reported to demonstrate its effectiveness. © Springer-Verlag 2013.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	20th International Conference, ICONIP 2013, Proceedings
Publisher	Springer Verlag
Pages	284-291
Volume	8226 LNCS
ISBN (Print)	9783642420535
DOIs	https://doi.org/10.1007/978-3-642-42054-2_36
Publication status	Published - 2013
Externally published	Yes
Event	20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Korea, Republic of Duration: 3 Nov 2013 → 7 Nov 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8226 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Neural Information Processing (ICONIP 2013)
Country/Territory	Korea, Republic of
City	Daegu
Period	3/11/13 → 7/11/13

Research Keywords

Global convergence
Recurrent neural networks
Robust pole assignment
State feedback control

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10.1007/978-3-642-42054-2_36

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Le, X., & Wang, J. (2013). A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation. In Neural Information Processing: 20th International Conference, ICONIP 2013, Proceedings (Vol. 8226 LNCS, pp. 284-291). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8226 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-42054-2_36

Le, Xinyi ; Wang, Jun. / A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation. Neural Information Processing: 20th International Conference, ICONIP 2013, Proceedings. Vol. 8226 LNCS Springer Verlag, 2013. pp. 284-291 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation",

abstract = "A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems is presented in this paper. The problem is reformulated from a quasi-convex optimization problem into a convex feasibility problem with the spectral condition number as the robustness measure. Two coupled globally convergent recurrent neural networks are applied for solving the reformulated problem in real time. Robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Simulation results of the proposed neurodynamic approach are reported to demonstrate its effectiveness. {\textcopyright} Springer-Verlag 2013.",

keywords = "Global convergence, Recurrent neural networks, Robust pole assignment, State feedback control",

author = "Xinyi Le and Jun Wang",

year = "2013",

doi = "10.1007/978-3-642-42054-2_36",

language = "English",

isbn = "9783642420535",

volume = "8226 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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booktitle = "Neural Information Processing",

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note = "20th International Conference on Neural Information Processing (ICONIP 2013) ; Conference date: 03-11-2013 Through 07-11-2013",

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Le, X & Wang, J 2013, A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation. in Neural Information Processing: 20th International Conference, ICONIP 2013, Proceedings. vol. 8226 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8226 LNCS, Springer Verlag, pp. 284-291, 20th International Conference on Neural Information Processing (ICONIP 2013), Daegu, Korea, Republic of, 3/11/13. https://doi.org/10.1007/978-3-642-42054-2_36

A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation. / Le, Xinyi; Wang, Jun.
Neural Information Processing: 20th International Conference, ICONIP 2013, Proceedings. Vol. 8226 LNCS Springer Verlag, 2013. p. 284-291 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8226 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation

AU - Le, Xinyi

AU - Wang, Jun

PY - 2013

Y1 - 2013

N2 - A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems is presented in this paper. The problem is reformulated from a quasi-convex optimization problem into a convex feasibility problem with the spectral condition number as the robustness measure. Two coupled globally convergent recurrent neural networks are applied for solving the reformulated problem in real time. Robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Simulation results of the proposed neurodynamic approach are reported to demonstrate its effectiveness. © Springer-Verlag 2013.

AB - A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems is presented in this paper. The problem is reformulated from a quasi-convex optimization problem into a convex feasibility problem with the spectral condition number as the robustness measure. Two coupled globally convergent recurrent neural networks are applied for solving the reformulated problem in real time. Robust parametric configuration and exact pole assignment of feedback control systems can be achieved. Simulation results of the proposed neurodynamic approach are reported to demonstrate its effectiveness. © Springer-Verlag 2013.

KW - Global convergence

KW - Recurrent neural networks

KW - Robust pole assignment

KW - State feedback control

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84893393294&origin=recordpage

U2 - 10.1007/978-3-642-42054-2_36

DO - 10.1007/978-3-642-42054-2_36

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783642420535

VL - 8226 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 284

EP - 291

BT - Neural Information Processing

PB - Springer Verlag

T2 - 20th International Conference on Neural Information Processing (ICONIP 2013)

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Le X, Wang J. A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation. In Neural Information Processing: 20th International Conference, ICONIP 2013, Proceedings. Vol. 8226 LNCS. Springer Verlag. 2013. p. 284-291. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-42054-2_36

A neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems based on a convex feasibility problem reformulation

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